Evaluation of Methyl-Binding Domain Based Enrichment Approaches Revisited

Methyl-binding domain (MBD) enrichment followed by deep sequencing (MBD-seq), is a robust and cost efficient approach for methylome-wide association studies (MWAS). MBD-seq has been demonstrated to be capable of identifying differentially methylated regions, detecting previously reported robust associations and producing findings that replicate with other technologies such as targeted pyrosequencing of bisulfite converted DNA. There are several kits commercially available that can be used for MBD enrichment. Our previous work has involved MethylMiner (Life Technologies, Foster City, CA, USA) that we chose after careful investigation of its properties. However, in a recent evaluation of five commercially available MBD-enrichment kits the performance of the MethylMiner was deemed poor. Given our positive experience with MethylMiner, we were surprised by this report. In an attempt to reproduce these findings we here have performed a direct comparison of MethylMiner with MethylCap (Diagenode Inc, Denville, NJ, USA), the best performing kit in that study. We find that both MethylMiner and MethylCap are two well performing MBD-enrichment kits. However, MethylMiner shows somewhat better enrichment efficiency and lower levels of background “noise”. In addition, for the purpose of MWAS where we want to investigate the majority of CpGs, we find MethylMiner to be superior as it allows tailoring the enrichment to the regions where most CpGs are located. Using targeted bisulfite sequencing we confirmed that sites where methylation was detected by either MethylMiner or by MethylCap indeed were methylated

Antioxidant, antiinflammatory and antiinvasive activities of biopolyphenolics

A large number of polyphenolic and heterocyclic compounds, i.e. 4-methylcoumarins, 4-methylthionocoumarins, xanthones, pyrazoles, pyrazolylacrylonitriles, flavones and isoflavones have been tested for their antioxidant activity towards NADPH-catalysed liver-microsomal lipid peroxidation with a view to establish their structure-activity relationship. Inhibition of microsomal lipid peroxidation by 7,8-dihydroxy-4-methylcoumarin (DHMC, 2) and 7,8-diacetoxy-4-methylcoumarin (DAMC, 3) was intriguing. We also found that dihydroxy and diacetoxy derivatives of 4-methylthionocoumarin were more potent in comparison to the corresponding coumarin derivatives in inhibiting TNF-α induced expression of ICAM-1. The effect of nine different xanthones has been examined on the modulation of cytokine-induced expression of ICAM-1 in human endothelial cells. 1,4-Dihydroxyxanthone (10) showed enhanced antioxidant activity as well as the inhibition of the expression of cell adhesion molecules, such as ICAM-1, VCAM-1 and E-selectin on endothelial cells in a concentration and time dependent manner. Antioxidant activity of different pyrazoles and pyrazolylacrylonitriles and antiinvasive activity of flavones and isoflavones against solid tumors have also been studied

Network analysis of human protein location

<p>Abstract</p> <p>Background</p> <p>Understanding cellular systems requires the knowledge of a protein's subcellular localization (SCL). Although experimental and predicted data for protein SCL are archived in various databases, SCL prediction remains a non-trivial problem in genome annotation. Current SCL prediction tools use amino-acid sequence features and text mining approaches. A comprehensive analysis of protein SCL in human PPI and metabolic networks for various subcellular compartments is necessary for developing a robust SCL prediction methodology.</p> <p>Results</p> <p>Based on protein-protein interaction (PPI) and metabolite-linked protein interaction (MLPI) networks of proteins, we have compared, contrasted and analysed the statistical properties across different subcellular compartments. We integrated PPI and metabolic datasets with SCL information of human proteins from LOCATE and GOA (Gene Ontology Annotation) and estimated three statistical properties: Chi-square (χ²) test, Paired Localisation Correlation Profile (PLCP) and network topological measures. For the PPI network, Pearson's chi-square test shows that for the same SCL category, twice as many interacting protein pairs are observed than estimated when compared to non-interacting protein pairs (χ²= 1270.19, <it>P-value </it>< 2.2 × 10^-16), whereas for MLPI, metabolite-linked protein pairs having the same SCL are observed 20% more than expected, compared to non-metabolite linked proteins (χ²= 110.02, <it>P-value </it>< 2.2 x10^-16). To address the issue of proteins with multiple SCLs, we have specifically used the PLCP (Pair Localization Correlation Profile) measure. PLCP analysis revealed that protein interactions are majorly restricted to the same SCL, though significant cross-compartment interactions are seen for nuclear proteins. Metabolite-linked protein pairs are restricted to specific compartments such as the mitochondrion (<it>P-value </it>< 6.0e-07), the lysosome (<it>P-value </it>< 4.7e-05) and the Golgi apparatus (<it>P-value </it>< 1.0e-15). These findings indicate that the metabolic network adds value to the information in the PPI network for the localisation process of proteins in human subcellular compartments.</p> <p>Conclusions</p> <p>The MLPI network differs significantly from the PPI network in its SCL distribution. The PPI network shows passive protein interaction, possibly due to its high false positive rate, across different subcellular compartments, which seem to be absent in the MLPI network, as the MLPI network has evolved to maintain high substrate specificity for proteins.</p

Small extracellular vesicles modulated by αVβ3 integrin induce neuroendocrine differentiation in recipient cancer cells

The ability of small extracellular vesicles (sEVs) to reprogram cancer cells is well established. However, the specific sEV components able to mediate aberrant effects in cancer cells have not been characterized. Integrins are major players in mediating sEV functions. We have previously reported that the αVβ3 integrin is detected in sEVs of prostate cancer (PαVβ3rCa) cells and transferred into recipient cells. Here, we investigate whether sEVs from -expressing cells affect tumour growth differently than sEVs from control cells that do not express αVβ3. We compared the ability of sEVs to stimulate tumour growth, using sEVs isolated from PrCa C4-2B cells by iodixanol density gradient and characterized with immunoblotting, nanoparticle tracking analysis, immunocapturing and single vesicle analysis. We incubated PrCa cells with sEVs and injected them subcutaneously into nude mice to measure in vivo tumour growth or analysed in vitro their anchorage-independent growth. Our results demonstrate that a single treatment with sEVs shed from C4-2B cells that express αVβ3, but not from control cells, stimulates tumour growth and induces differentiation of PrCa cells towards a neuroendocrine phenotype, as quantified by increased levels of neuroendocrine markers. In conclusion, the expression of αVβ3 integrin generates sEVs capable of reprogramming cells towards an aggressive phenotype

A unique view of SARS-COV-2 through the lens of ORF8 protein

Immune evasion is one of the unique characteristics of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) attributed to its ORF8 protein. This protein modulates the adaptive host immunity through down-regulation of MHC-1 (Major Histocompatibility Complex) molecules and innate immune responses by surpassing the host\u27s interferon-mediated antiviral response. To understand the host\u27s immune perspective in reference to the ORF8 protein, a comprehensive study of the ORF8 protein and mutations possessed by it have been performed. Chemical and structural properties of ORF8 proteins from different hosts, such as human, bat, and pangolin, suggest that the ORF8 of SARS-CoV-2 is much closer to ORF8 of Bat RaTG13-CoV than to that of Pangolin-CoV. Eighty-seven mutations across unique variants of ORF8 in SARS-CoV-2 can be grouped into four classes based on their predicted effects (Hussain et al., 2021) [1]. Based on the geo-locations and timescale of sample collection, a possible flow of mutations was built. Furthermore, conclusive flows of amalgamation of mutations were found upon sequence similarity analyses and consideration of the amino acid conservation phylogenies. Therefore, this study seeks to highlight the uniqueness of the rapidly evolving SARS-CoV-2 through the ORF8

Incorporating functional inter-relationships into protein function prediction algorithms

<p>Abstract</p> <p>Background</p> <p>Functional classification schemes (e.g. the Gene Ontology) that serve as the basis for annotation efforts in several organisms are often the source of gold standard information for computational efforts at supervised protein function prediction. While successful function prediction algorithms have been developed, few previous efforts have utilized more than the protein-to-functional class label information provided by such knowledge bases. For instance, the Gene Ontology not only captures protein annotations to a set of functional classes, but it also arranges these classes in a DAG-based hierarchy that captures rich inter-relationships between different classes. These inter-relationships present both opportunities, such as the potential for additional training examples for small classes from larger related classes, and challenges, such as a harder to learn distinction between similar GO terms, for standard classification-based approaches.</p> <p>Results</p> <p>We propose a method to enhance the performance of classification-based protein function prediction algorithms by addressing the issue of using these interrelationships between functional classes constituting functional classification schemes. Using a standard measure for evaluating the semantic similarity between nodes in an ontology, we quantify and incorporate these inter-relationships into the <it>k</it>-nearest neighbor classifier. We present experiments on several large genomic data sets, each of which is used for the modeling and prediction of over hundred classes from the GO Biological Process ontology. The results show that this incorporation produces more accurate predictions for a large number of the functional classes considered, and also that the classes benefitted most by this approach are those containing the fewest members. In addition, we show how our proposed framework can be used for integrating information from the entire GO hierarchy for improving the accuracy of predictions made over a set of base classes. Finally, we provide qualitative and quantitative evidence that this incorporation of functional inter-relationships enables the discovery of interesting biology in the form of novel functional annotations for several yeast proteins, such as Sna4, Rtn1 and Lin1.</p> <p>Conclusion</p> <p>We implemented and evaluated a methodology for incorporating interrelationships between functional classes into a standard classification-based protein function prediction algorithm. Our results show that this incorporation can help improve the accuracy of such algorithms, and help uncover novel biology in the form of previously unknown functional annotations. The complete source code, a sample data set and the additional files for this paper are available free of charge for non-commercial use at <url>http://www.cs.umn.edu/vk/gaurav/functionalsimilarity/</url>.</p

Methylome-Wide Association Study of Schizophrenia: Identifying Blood Biomarker Signatures of Environmental Insults

Epigenetic studies present unique opportunities to advance schizophrenia research because they can potentially account for many of its clinical features and suggest novel strategies to improve disease management